






'''simple docstring'''







import random

import torch
from huggingface_hub import HfApi

from diffusers import UNetaDModel


UpperCamelCase     :   Dict	      =		HfApi()

UpperCamelCase     :   Any	      =		{}
# fmt: off
UpperCamelCase     :   str	      =		torch.tensor([
    -0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,
    1.2342, -2.2485, 0.4636, 0.8076, -0.7991, 0.3969, 0.8498, 0.9189,
    -1.8887, -3.3522, 0.7639, 0.2040, 0.6271, -2.7148, -1.6316, 3.0839,
    0.3186, 0.2721, -0.9759, -1.2461, 2.6257, 1.3557
])
UpperCamelCase     :   Dict	      =		torch.tensor([
    -2.3639, -2.5344, 0.0054, -0.6674, 1.5990, 1.0158, 0.3124, -2.1436,
    1.8795, -2.5429, -0.1566, -0.3973, 1.2490, 2.6447, 1.2283, -0.5208,
    -2.8154, -3.5119, 2.3838, 1.2033, 1.7201, -2.1256, -1.4576, 2.7948,
    2.4204, -0.9752, -1.2546, 0.8027, 3.2758, 3.1365
])
UpperCamelCase     :   Optional[int]	      =		torch.tensor([
    -0.6531, -0.6891, -0.3172, -0.5375, -0.9140, -0.5367, -0.1175, -0.7869,
    -0.3808, -0.4513, -0.2098, -0.0083, 0.3183, 0.5140, 0.2247, -0.1304,
    -0.1302, -0.2802, -0.2084, -0.2025, -0.4967, -0.4873, -0.0861, 0.6925,
    0.0250, 0.1290, -0.1543, 0.6316, 1.0460, 1.4943
])
UpperCamelCase     :   Any	      =		torch.tensor([
    0.0911, 0.1107, 0.0182, 0.0435, -0.0805, -0.0608, 0.0381, 0.2172,
    -0.0280, 0.1327, -0.0299, -0.0255, -0.0050, -0.1170, -0.1046, 0.0309,
    0.1367, 0.1728, -0.0533, -0.0748, -0.0534, 0.1624, 0.0384, -0.1805,
    -0.0707, 0.0642, 0.0220, -0.0134, -0.1333, -0.1505
])
UpperCamelCase     :   Tuple	      =		torch.tensor([
    0.1321, 0.1337, 0.0440, 0.0622, -0.0591, -0.0370, 0.0503, 0.2133,
    -0.0177, 0.1415, -0.0116, -0.0112, 0.0044, -0.0980, -0.0789, 0.0395,
    0.1502, 0.1785, -0.0488, -0.0514, -0.0404, 0.1539, 0.0454, -0.1559,
    -0.0665, 0.0659, 0.0383, -0.0005, -0.1266, -0.1386
])
UpperCamelCase     :   Optional[int]	      =		torch.tensor([
    0.1154, 0.1218, 0.0307, 0.0526, -0.0711, -0.0541, 0.0366, 0.2078,
    -0.0267, 0.1317, -0.0226, -0.0193, -0.0014, -0.1055, -0.0902, 0.0330,
    0.1391, 0.1709, -0.0562, -0.0693, -0.0560, 0.1482, 0.0381, -0.1683,
    -0.0681, 0.0661, 0.0331, -0.0046, -0.1268, -0.1431
])
UpperCamelCase     :   List[str]	      =		torch.tensor([
    0.1192, 0.1240, 0.0414, 0.0606, -0.0557, -0.0412, 0.0430, 0.2042,
    -0.0200, 0.1385, -0.0115, -0.0132, 0.0017, -0.0965, -0.0802, 0.0398,
    0.1433, 0.1747, -0.0458, -0.0533, -0.0407, 0.1545, 0.0419, -0.1574,
    -0.0645, 0.0626, 0.0341, -0.0010, -0.1199, -0.1390
])
UpperCamelCase     :   List[Any]	      =		torch.tensor([
    0.1075, 0.1074, 0.0205, 0.0431, -0.0774, -0.0607, 0.0298, 0.2042,
    -0.0320, 0.1267, -0.0281, -0.0250, -0.0064, -0.1091, -0.0946, 0.0290,
    0.1328, 0.1650, -0.0580, -0.0738, -0.0586, 0.1440, 0.0337, -0.1746,
    -0.0712, 0.0605, 0.0250, -0.0099, -0.1316, -0.1473
])
UpperCamelCase     :   Union[str, Any]	      =		torch.tensor([
    -1.4572, -2.0481, -0.0414, -0.6005, 1.4136, 0.5848, 0.4028, -2.7330,
    1.2212, -2.1228, 0.2155, 0.4039, 0.7662, 2.0535, 0.7477, -0.3243,
    -2.1758, -2.7648, 1.6947, 0.7026, 1.2338, -1.6078, -0.8682, 2.2810,
    1.8574, -0.5718, -0.5586, -0.0186, 2.3415, 2.1251])
UpperCamelCase     :   Optional[Any]	      =		torch.tensor([
    -1.3690, -1.9720, -0.4090, -0.6966, 1.4660, 0.9938, -0.1385, -2.7324,
    0.7736, -1.8917, 0.2923, 0.4293, 0.1693, 1.4112, 1.1887, -0.3181,
    -2.2160, -2.6381, 1.3170, 0.8163, 0.9240, -1.6544, -0.6099, 2.5259,
    1.6430, -0.9090, -0.9392, -0.0126, 2.4268, 2.3266
])
UpperCamelCase     :   str	      =		torch.tensor([
    -1.3525, -1.9628, -0.3956, -0.6860, 1.4664, 1.0014, -0.1259, -2.7212,
    0.7772, -1.8811, 0.2996, 0.4388, 0.1704, 1.4029, 1.1701, -0.3027,
    -2.2053, -2.6287, 1.3350, 0.8131, 0.9274, -1.6292, -0.6098, 2.5131,
    1.6505, -0.8958, -0.9298, -0.0151, 2.4257, 2.3355
])
UpperCamelCase     :   str	      =		torch.tensor([
    -2.0585, -2.7897, -0.2850, -0.8940, 1.9052, 0.5702, 0.6345, -3.8959,
    1.5932, -3.2319, 0.1974, 0.0287, 1.7566, 2.6543, 0.8387, -0.5351,
    -3.2736, -4.3375, 2.9029, 1.6390, 1.4640, -2.1701, -1.9013, 2.9341,
    3.4981, -0.6255, -1.1644, -0.1591, 3.7097, 3.2066
])
UpperCamelCase     :   int	      =		torch.tensor([
    -2.3139, -2.5594, -0.0197, -0.6785, 1.7001, 1.1606, 0.3075, -2.1740,
    1.8071, -2.5630, -0.0926, -0.3811, 1.2116, 2.6246, 1.2731, -0.5398,
    -2.8153, -3.6140, 2.3893, 1.3262, 1.6258, -2.1856, -1.3267, 2.8395,
    2.3779, -1.0623, -1.2468, 0.8959, 3.3367, 3.2243
])
UpperCamelCase     :   List[str]	      =		torch.tensor([
    -2.0628, -2.7667, -0.2089, -0.8263, 2.0539, 0.5992, 0.6495, -3.8336,
    1.6025, -3.2817, 0.1721, -0.0633, 1.7516, 2.7039, 0.8100, -0.5908,
    -3.2113, -4.4343, 2.9257, 1.3632, 1.5562, -2.1489, -1.9894, 3.0560,
    3.3396, -0.7328, -1.0417, 0.0383, 3.7093, 3.2343
])
UpperCamelCase     :   List[str]	      =		torch.tensor([
    -1.4574, -2.0569, -0.0473, -0.6117, 1.4018, 0.5769, 0.4129, -2.7344,
    1.2241, -2.1397, 0.2000, 0.3937, 0.7616, 2.0453, 0.7324, -0.3391,
    -2.1746, -2.7744, 1.6963, 0.6921, 1.2187, -1.6172, -0.8877, 2.2439,
    1.8471, -0.5839, -0.5605, -0.0464, 2.3250, 2.1219
])
# fmt: on

UpperCamelCase     :   str	      =		api.list_models(filter='diffusers')
for mod in models:
							if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
														UpperCamelCase     :   List[Any]	      =		'/home/patrick/google_checkpoints/' + mod.modelId.split('/')[-1]

														print(F'Started running {mod.modelId}!!!')

														if mod.modelId.startswith('CompVis'):
																					UpperCamelCase     :   Dict	      =		UNetaDModel.from_pretrained(local_checkpoint, subfolder='unet')
														else:
																					UpperCamelCase     :   Any	      =		UNetaDModel.from_pretrained(local_checkpoint)

														torch.manual_seed(0)
														random.seed(0)

														UpperCamelCase     :   str	      =		torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
														UpperCamelCase     :   List[str]	      =		torch.tensor([10] * noise.shape[0])
														with torch.no_grad():
																					UpperCamelCase     :   Union[str, Any]	      =		model(noise, time_step).sample

														assert torch.allclose(
														    logits[0, 0, 0, :30], results['_'.join('_'.join(mod.modelId.split('/')).split('-'))], atol=1E-3
														)
														print(F'{mod.modelId} has passed successfully!!!')





'''simple docstring'''


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase_						:  List[str]				  =						logging.get_logger(__name__)

UpperCAmelCase_						:  Optional[Any]				  =						{
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.linear_k': 'encoder.layers.*.self_attn.linear_k',
    'self_attn.linear_v': 'encoder.layers.*.self_attn.linear_v',
    'self_attn.linear_q': 'encoder.layers.*.self_attn.linear_q',
    'self_attn.pos_bias_u': 'encoder.layers.*.self_attn.pos_bias_u',
    'self_attn.pos_bias_v': 'encoder.layers.*.self_attn.pos_bias_v',
    'self_attn.linear_out': 'encoder.layers.*.self_attn.linear_out',
    'self_attn.linear_pos': 'encoder.layers.*.self_attn.linear_pos',
    'self_attn.rotary_emb': 'encoder.embed_positions',
    'self_attn_layer_norm': 'encoder.layers.*.self_attn_layer_norm',
    'conv_module.pointwise_conv1': 'encoder.layers.*.conv_module.pointwise_conv1',
    'conv_module.pointwise_conv2': 'encoder.layers.*.conv_module.pointwise_conv2',
    'conv_module.depthwise_conv': 'encoder.layers.*.conv_module.depthwise_conv',
    'conv_module.batch_norm': 'encoder.layers.*.conv_module.batch_norm',
    'conv_module.layer_norm': 'encoder.layers.*.conv_module.layer_norm',
    'ffn1.w_1': 'encoder.layers.*.ffn1.intermediate_dense',
    'ffn1.w_2': 'encoder.layers.*.ffn1.output_dense',
    'ffn1.layer_norm': 'encoder.layers.*.ffn1_layer_norm',
    'ffn2.w_1': 'encoder.layers.*.ffn2.intermediate_dense',
    'ffn2.w_2': 'encoder.layers.*.ffn2.output_dense',
    'ffn2.layer_norm': 'encoder.layers.*.ffn2_layer_norm',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
UpperCAmelCase_						:  Tuple				  =						[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				for attribute in key.split("""."""	):
								_SCREAMING_SNAKE_CASE					:			List[Any]							= getattr(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)

				if weight_type is not None:
								_SCREAMING_SNAKE_CASE					:			int							= getattr(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	).shape
				else:
								_SCREAMING_SNAKE_CASE					:			Any							= hf_pointer.shape

				if hf_shape != value.shape:
								raise ValueError(
								    f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
								    f""" {value.shape} for {full_name}"""	)

				if weight_type == "weight":
								_SCREAMING_SNAKE_CASE					:			int							= value
				elif weight_type == "weight_g":
								_SCREAMING_SNAKE_CASE					:			List[str]							= value
				elif weight_type == "weight_v":
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= value
				elif weight_type == "bias":
								_SCREAMING_SNAKE_CASE					:			Tuple							= value
				elif weight_type == "running_mean":
								_SCREAMING_SNAKE_CASE					:			Tuple							= value
				elif weight_type == "running_var":
								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= value
				elif weight_type == "num_batches_tracked":
								_SCREAMING_SNAKE_CASE					:			Any							= value
				elif weight_type == "inv_freq":
								_SCREAMING_SNAKE_CASE					:			List[str]							= value
				else:
								_SCREAMING_SNAKE_CASE					:			List[Any]							= value

				logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""	)







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			List[Any]							= []
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= fairseq_model.state_dict()

				_SCREAMING_SNAKE_CASE					:			Any							= hf_model.wavaveca_conformer.feature_extractor

				for name, value in fairseq_dict.items():
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= False
								if "conv_layers" in name:
												load_conv_layer(
												    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, hf_model.config.feat_extract_norm == """group"""				, )
												_SCREAMING_SNAKE_CASE					:			str							= True
								else:
												for key, mapped_key in MAPPING.items():
																_SCREAMING_SNAKE_CASE					:			List[Any]							= """wav2vec2_conformer.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
																if key in name or key.split("""w2v_model."""	)[-1] == name.split("""."""	)[0]:
																				_SCREAMING_SNAKE_CASE					:			List[str]							= True
																				if "*" in mapped_key:
																								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= name.split(SCREAMING_SNAKE_CASE__	)[0].split("""."""	)[-2]
																								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= mapped_key.replace("""*"""				, SCREAMING_SNAKE_CASE__	)
																				if "pos_bias_u" in name:
																								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= None
																				elif "pos_bias_v" in name:
																								_SCREAMING_SNAKE_CASE					:			Optional[int]							= None
																				elif "weight_g" in name:
																								_SCREAMING_SNAKE_CASE					:			int							= """weight_g"""
																				elif "weight_v" in name:
																								_SCREAMING_SNAKE_CASE					:			Any							= """weight_v"""
																				elif "bias" in name:
																								_SCREAMING_SNAKE_CASE					:			Dict							= """bias"""
																				elif "weight" in name:
																								# TODO: don't match quantizer.weight_proj
																								_SCREAMING_SNAKE_CASE					:			int							= """weight"""
																				elif "running_mean" in name:
																								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= """running_mean"""
																				elif "inv_freq" in name:
																								_SCREAMING_SNAKE_CASE					:			int							= """inv_freq"""
																				elif "running_var" in name:
																								_SCREAMING_SNAKE_CASE					:			int							= """running_var"""
																				elif "num_batches_tracked" in name:
																								_SCREAMING_SNAKE_CASE					:			Tuple							= """num_batches_tracked"""
																				else:
																								_SCREAMING_SNAKE_CASE					:			Dict							= None
																				set_recursively(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
																continue
								if not is_used:
												unused_weights.append(SCREAMING_SNAKE_CASE__	)

				logger.warning(f"""Unused weights: {unused_weights}"""	)







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			int							= full_name.split("""conv_layers."""	)[-1]
				_SCREAMING_SNAKE_CASE					:			Tuple							= name.split("""."""	)
				_SCREAMING_SNAKE_CASE					:			Any							= int(items[0]	)
				_SCREAMING_SNAKE_CASE					:			str							= int(items[1]	)

				if type_id == 0:
								if "bias" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																raise ValueError(
																    f"""{full_name} has size {value.shape}, but"""
																    f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""	)
												_SCREAMING_SNAKE_CASE					:			Tuple							= value
												logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""	)
								elif "weight" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																raise ValueError(
																    f"""{full_name} has size {value.shape}, but"""
																    f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""	)
												_SCREAMING_SNAKE_CASE					:			Optional[int]							= value
												logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""	)
				elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
								if "bias" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																raise ValueError(
																    f"""{full_name} has size {value.shape}, but"""
																    f""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""	)
												_SCREAMING_SNAKE_CASE					:			List[Any]							= value
												logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
								elif "weight" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																raise ValueError(
																    f"""{full_name} has size {value.shape}, but"""
																    f""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""	)
												_SCREAMING_SNAKE_CASE					:			str							= value
												logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
				else:
								unused_weights.append(SCREAMING_SNAKE_CASE__	)







@torch.no_grad()
def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__=None				, SCREAMING_SNAKE_CASE__=None				, SCREAMING_SNAKE_CASE__=True	):







				"""simple docstring"""





				if config_path is not None:
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= WavaVecaConformerConfig.from_pretrained(SCREAMING_SNAKE_CASE__				, hidden_act="""swish"""	)
				else:
								_SCREAMING_SNAKE_CASE					:			List[str]							= WavaVecaConformerConfig()

				if "rope" in checkpoint_path:
								_SCREAMING_SNAKE_CASE					:			List[str]							= """rotary"""

				if is_finetuned:
								if dict_path:
												_SCREAMING_SNAKE_CASE					:			List[str]							= Dictionary.load(SCREAMING_SNAKE_CASE__	)

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												_SCREAMING_SNAKE_CASE					:			Optional[int]							= target_dict.pad_index
												_SCREAMING_SNAKE_CASE					:			int							= target_dict.bos_index
												_SCREAMING_SNAKE_CASE					:			Any							= target_dict.eos_index
												_SCREAMING_SNAKE_CASE					:			List[str]							= len(target_dict.symbols	)
												_SCREAMING_SNAKE_CASE					:			Dict							= os.path.join(SCREAMING_SNAKE_CASE__				, """vocab.json"""	)
												if not os.path.isdir(SCREAMING_SNAKE_CASE__	):
																logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(SCREAMING_SNAKE_CASE__	)	)
																return
												os.makedirs(SCREAMING_SNAKE_CASE__				, exist_ok=SCREAMING_SNAKE_CASE__	)
												_SCREAMING_SNAKE_CASE					:			Dict							= target_dict.indices

												# fairseq has the <pad> and <s> switched
												_SCREAMING_SNAKE_CASE					:			Optional[Any]							= 0
												_SCREAMING_SNAKE_CASE					:			List[Any]							= 1
												with open(SCREAMING_SNAKE_CASE__				, """w"""				, encoding="""utf-8"""	) as vocab_handle:
																json.dump(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
												_SCREAMING_SNAKE_CASE					:			Tuple							= WavaVecaCTCTokenizer(
												    SCREAMING_SNAKE_CASE__				, unk_token=target_dict.unk_word				, pad_token=target_dict.pad_word				, bos_token=target_dict.bos_word				, eos_token=target_dict.eos_word				, word_delimiter_token="""|"""				, do_lower_case=SCREAMING_SNAKE_CASE__				, )
												_SCREAMING_SNAKE_CASE					:			Optional[int]							= True if config.feat_extract_norm == """layer""" else False
												_SCREAMING_SNAKE_CASE					:			Dict							= WavaVecaFeatureExtractor(
												    feature_size=1				, sampling_rate=1_6000				, padding_value=0				, do_normalize=SCREAMING_SNAKE_CASE__				, return_attention_mask=SCREAMING_SNAKE_CASE__				, )
												_SCREAMING_SNAKE_CASE					:			Optional[int]							= WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE__				, tokenizer=SCREAMING_SNAKE_CASE__	)
												processor.save_pretrained(SCREAMING_SNAKE_CASE__	)

								_SCREAMING_SNAKE_CASE					:			str							= WavaVecaConformerForCTC(SCREAMING_SNAKE_CASE__	)
				else:
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= WavaVecaConformerForPreTraining(SCREAMING_SNAKE_CASE__	)

				if is_finetuned:
								_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Optional[int]							= fairseq.checkpoint_utils.load_model_ensemble_and_task(
								    [checkpoint_path]				, arg_overrides={"""data""": """/""".join(dict_path.split("""/"""	)[:-1]	)}	)
				else:
								_SCREAMING_SNAKE_CASE					:			Tuple							= argparse.Namespace(task="""audio_pretraining"""	)
								_SCREAMING_SNAKE_CASE					:			List[str]							= fairseq.tasks.setup_task(SCREAMING_SNAKE_CASE__	)

								_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Optional[Any]							= fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				, task=SCREAMING_SNAKE_CASE__	)

				_SCREAMING_SNAKE_CASE					:			Optional[int]							= model[0].eval()

				recursively_load_weights(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, not is_finetuned	)

				hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
				UpperCAmelCase_						:  List[Any]				  =						argparse.ArgumentParser()
				parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
				parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
				parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
				parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
				parser.add_argument(
				    '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
				)
				UpperCAmelCase_						:  List[Any]				  =						parser.parse_args()
				convert_wavaveca_conformer_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
				)




